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We present the first report on reflectometric optical fiber sensing based on mechanically induced cascaded long-period grating (LPG) structure. This method utilizes in-house designed mechanical casings and a bare fiber. When a fiber is sandwiched between the casings and pressed by tightening screws, an LPG is induced due to a photo-elastic effect. By exploiting Fresnel refection at a fiber end, a cascaded LPG structure can be implemented using a single LPG, enabling reflectometric configuration. When this sensor is subjected to external physical processes, the attenuation bands change clearly, and their shift can be used for non-destructive evaluations. We show experimentally that this sensor can measure loads of up to 10 N without producing any permanent change in the fiber properties, indicating its pressure sensing capability. We also show that this sensor can potentially measure strain and temperature. In our experiments, the strain and temperature sensitivities are 9.4 nm/% and 0.045 nm/°C, respectively.

As the cracking mode (tensile or shear) of a crack is related to the underlying physical mechanisms, crack mode classification is a very useful method to identify the damage state of a structure for proper maintenance to enhance structural safety and durability. Acoustic Emission (AE) is a passive structural health monitoring technique based on the stress wave generated due to cracking in a structure. A framework has been designed in this study for automated probabilistic classification of the cracks in cementitious components based on the AE signals. With this approach, unlabeled hand designed waveform parameters, i.e. RA values (RA) and Average frequency (AF) are clustered using density dictated unsupervised clustering algorithm. Intersecting clusters in the data were then separated through a hyperplane created using Support Vector Machine (SVM) algorithm. Based on physical insight obtained from labeled data, unlabeled data was classified into events corresponding to different cracking modes. The framework was applied to the analysis of AE data from Steel Fiber Reinforced Concrete (SFRC) beam under bending and Strain Hardening Cementitious Composite (SHCC) samples under direct tension. The cracking modes obtained from the proposed machine learning approach are found to be in good agreement with expectations based on composite theory. With good qualitative prediction, the proposed approach shows promise for the prediction of damage state in structures based on unlabeled data obtained in the field.

A non-contact method was proposed to monitor the freshness (based on TVB-N and TBA values) of large yellow croaker fillets (Larimichthys crocea) by using a visible and near-infrared hyperspectral imaging system (400–1000 nm). In this work, the quantitative calibration models were built by using feed-forward neural networks (FNN) and partial least squares regression (PLSR). In addition, it was established that using a regression coefficient on the data can be further compressed by selecting optimal wavelengths (35 for TVB-N and 18 for TBA). The results validated that FNN has higher prediction accuracies than PLSR for both cases using full and selected reflectance spectra. Moreover, our FNN based model has showcased excellent performance even with selected reflectance spectra with rp = 0.978, R2p = 0.981, and RMSEP = 2.292 for TVB-N, and rp = 0.957, R2p = 0.916, and RMSEP = 0.341 for TBA, respectively. This optimal FNN model was then utilized for pixel-wise visualization maps of TVB-N and TBA contents in fillets.

Characterization of surface cracks is one of the key steps towards condititional assessment and understanding the durability of strain hardening cementitious composites (SHCCs). Under laboratory conditions, surface crack statistics can be obtained from images of specimen surfaces through manual inspection. This is subjective, time consuming and laborious. In order to automate this process a framework encompassing a deep learning model for rapid identification and computation of crack parameters of thin SHCC cracks in presented in this work. A tailored deep convolutiontional neural network (TDCNN) was trained to detect thin cracks and then crack parameters were computed using image processing technique. The proposed technique does not require optimal lighting conditions, proper surface treatment, and prior (manual) selection of the correct region for proper inference.. The results from the controlled study suggest that the inference ability of TDCNN is reasonably good, resilient against epistemic uncertainty and tunable for completely independent but adverse observations. From the crack pattern computed using TDCCN, crack parameters-average crack width (ACW) and crack density (CD) can be calculated to facilitate conditional and durability assessment in a practical environment.

In IoT applications with remote monitoring, the data is transferred through a wireless channel. To ensure data reliability, error control coding is essential. For the high error correcting ability, the Low density parity check (LDPC) codes are used as the most popular code for error-control in many domains like telecommunication, magnetic-recording, radio application, etc. Thus the data transmitted in IoT system may be encoded by LDPC codes. This paper presents a small length LDPC code of six-by-seven. The shifted identity matrix and a lower staircase matrix are used to construct the LDPC code. These codes have very low encoding complexity. Belief-propagation is used to decode the code. In the simulation result of the code, improved performance in bit error rate is shown. The simulation result of these codes shows a good performance in the bit-error-rate.

The paper presents a brief survey on the underwater acoustic communication channel estimation of orthogonal frequency division multiplexing using deep learning techniques. In recent times, deep learning has shown great potential in the field of communication. The underwater acoustic channel is a highly challenging system as it has a restricted bandwidth, enlarged multi-path, medium refractive characteristics, acute fading, rapid time variation, large Doppler shifts, etc. For the UWA-OFDM communication model, DL-based receivers can be elucidated as a deep neural network for a better estimation than the conventional techniques. Sufficient training is required for the recovery of the transmitted symbols. It does not require any explicit CE or equalization and balancing. The estimation is accomplished in two stages. The first one is training stage. For training, this deep neural network-transmitted data, the signal received in the unknown channel, etc., are used as labeled data. The second one is test stage, where the deep neural network receiver recovers transmitted symbols, given the received signal. The accuracy and the computational complexity, these two have been a great challenge for the underwater acoustic channel estimation with the existing traditional channel estimation methods. The deep learning method is useful to solve these problems. This survey is to find out a few optimum methods of deep learning to estimate the UWA channel in an efficient way.

Strain Hardening cementitious composites (SHCCs) have been known to produce excellent crack width control and very good tensile strain capacity. As a result, strategically introducing SHCCs would lead to significant carbon saving through the creation of structures that are durable and could potentially undergo autogenous self-healing. However, for wider acceptability of any new material requires long term empirical data. This may be circumvented by continually ensuring a structure made with this new material is serviceable i.e. material has not failed. We present an approach to predict failure time without any prior information on operating conditions and material information for SHCCs. This method is based on the modeling of ‘finite time singularity’ based on mathematical laws. The method produces fairly accurate results when applied to estimate failure for SHCCs with a wide range of failure times and to both linear and non-linear loading regimes. The practical applicability of this method was thus demonstrated.

The use of smart cementitious materials is becoming increasingly critical for the enhanced serviceability of structures. The addition of carbon fibers, carbon nanotubes, and various nano-powders such as nano-silica, carbon black, and graphite giving cementitious materials electrical properties that can be used for self-sensing has been known for almost two decades. Many sensing principles and techniques using smart materials have been successfully developed and applied mostly in laboratory testing over last few decades. The strong capacity of Fiber-Reinforced Cementitious Composites for autogenous healing in addition to crack control (especially in the case of Strain-Hardening Cementitious Composites) has been reported by many researchers. Similarly, the applications of different mineral and bio-additive materials to achieve the self-healing of cracks have been noted with great interest. Design for serviceability based on the durability of the materials used in concrete structures is often neglected. With durability performance testing becoming more sophisticated, detailed service life design is being demanded in the most important infrastructure projects. The present review is focused on identifying field applications and highlighting the Performance-Driven Design Approach for tailoring material solutions for the problems likely to be faced by civil infrastructures in the future. A real-life case study is presented to illustrate the minimal cost implications of adopting the latest smart material for an eco-friendly, durable, reliable, and resilient infrastructure. Identifying critical challenges faced by the industry and developing solutions for the same is going to help bridge the current gaps between research and adoption.